CN109165858A - Multi-satellite scheduling method for large-area target observation - Google Patents

Multi-satellite scheduling method for large-area target observation Download PDF

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CN109165858A
CN109165858A CN201811029393.3A CN201811029393A CN109165858A CN 109165858 A CN109165858 A CN 109165858A CN 201811029393 A CN201811029393 A CN 201811029393A CN 109165858 A CN109165858 A CN 109165858A
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CN109165858B (en
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刘晓路
许英杰
姚锋
陈盈果
陈成
王涛
吕济民
陈宇宁
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National University of Defense Technology
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Abstract

The invention provides a multi-satellite scheduling method for large-area target observation, aiming at the problem of multi-satellite scheduling for large-area observation. Firstly, according to the requirements of users, determining an observation task, namely determining a target region T to be observed (namely the position and the area of a known target region); satellite parameter information is determined for the set of available satellites S and for all available satellites in the set of available satellites S. Then, the target area is discretized, and the target area is subjected to stripe decomposition. The invention converts the multi-satellite scheduling problem facing the large-area target observation into a set coverage problem with a plurality of constraints and establishes a mathematical model. And finally, solving the mathematical model established in the S4 by adopting a genetic algorithm, and outputting an observation plan obtained by solving. The invention solves the problems of long time consumption and coverage omission of the satellite image acquisition method related to the current environmental protection activities, and a feasible observation scheme with higher target coverage rate for a specific observation area can be obtained by the method.

Description

More star dispatching methods towards big polygon target observing
Technical field
The present invention relates to satellite dispatching technique field more particularly to a kind of more star dispatching parties towards big polygon target observing Method.
Background technique
Earth observation satellite (EOS) is the space that earth surface specific region image can be obtained according to various observation requirements Plateform system.Since it has the characteristics that high reliablity, convenient and efficient, EOS Image Acquisition is by government organs, research institution It is widely recognized as with numerous clients such as commercial enterprise.In recent years, since mankind's activity is frequent, natural environment worsening, natural calamity Evil frequently occurs, and environmental protection activity is increasingly taken seriously, such as land mapping, environmental monitoring, desertification monitoring, the torrid zone The activities such as rainforest protection, Aspect On Study of Antarctic Ice Cap monitoring expansion etc. in succession in the world.EOS also plays more next in these activities More important role, point, the observation with different imaging angles to regional aim in different times may be implemented in it, and this is just It is the reliable basis analyzing environmental aspect and in case of emergency taking appropriate measures.Therefore, it is effectively treated in observation process Imaging Scheduling problem will further push the movable development of environmental protection to obtain enough images.
However, traditional technology and method fail to cope with the moonscope scheduling problem in these environmental activities and are difficult to short Satisfactory observation program is generated in time.On the one hand, it is usually directed to target for the mapping of natural environment and monitoring activity Area is very big, such as Amazon rain forest area and Aspect On Study of Antarctic Ice Cap.This means that the imager of satellite can be than relatively small region It is more, and candidate observation time window is caused to be exponentially increased.In addition, the increase of client and quantity required will lead to scheduling problem Complexity abruptly increase.On the other hand, it is contemplated that a variety of satellite characteristics are different, such as satellite sensor breadth, maneuverability, defend The traffic direction etc. of star, this adds increased the complexity of scheduling problem.
The prior art includes presently disclosed document, is all directed to compare pinpoint target, dabble large area target It is less.As the expansion of observed object area, the observation requirements from different clients increase, search space and candidate solutions Be incremented by, observation window selection and scheduling are more freely changeable, lead to generate that feasible surveillance program is difficult and consuming time is long. Therefore, the satellite scheduling problem of coverage count in the urgent need to address.
Summary of the invention
For more satellite scheduling problems towards big area observation, the present invention provides a kind of towards big polygon target observing More star dispatching methods.
To realize the above-mentioned technical purpose, the technical scheme is that
A kind of more star dispatching methods towards big polygon target observing, include the following steps；
S1 determines that observation mission determines target area T to be observed according to user demand；Determine usable satellite set S And in usable satellite set S all usable satellites satellite parametric reduction information.
The target area S2 discretization
Target area T to be observed is passed through into the equidistant discretization of dot matrix, all discrete points in target area T to be observed Form point set A.Each point in point set A is a point target, point target piPosition coordinates useIt indicates, Indicate the latitude of its point target in 84 coordinate system of WGS, longitude and altitude.Wherein i=1,2,3 ... n, n are in point set A The number of point target.In this way, target area T to be observed can be indicated with a series of point target.
S3 carries out band decomposition to target area
S3.1 constructor F describes the positional relationship between satellite and point target
When moment t, for the usable satellite S in usable satellite set Sj(wherein j=1,2,3 ... m, m are usable satellite The number of usable satellite in set S.), usable satellite SjIts spatial position in J2000 geocentric inertial coordinate system isPoint target piIt is in the spatial position of WGS84 coordinate systemIt can be with defending Star SjIt can observe point target piCurrent pose beThen have
Constructor F describes the positional relationship between satellite and point target
Independent variable in function F can be abbreviated as (t, r)T, referred to as when m- orientation vectorThrough target area discretization in S2 Method, target area T to be observed can be indicated with a series of point target.Therefore, target area T to be observed can be indicated Are as follows: { (t, r)1,(t,r)2,...,(t,r)n}。
S3.2 is for each usable satellite S in usable satellite set Sj, calculate SjVisible range to target area T is Visual field (FOV), and calculate the bounding rectangles of FOV Yu the overlapping region target area T.
For each usable satellite S in usable satellite set SjBounding rectangles corresponding to it, the determination of bounding rectangles Intersection point e between FOV and target area T is associated, which can be calculated with spherical surface analytic geometry.
With usable satellite SjSatellite ground tracks (i.e. usable satellite SjStar it is offline) parallel rectangular edges are usable satellites SjThe rectangular edges of the length direction of bounding rectangles corresponding to it, perpendicular to usable satellite SjSatellite ground tracks it is (i.e. available to defend Star SjStar it is offline) side be usable satellite SjThe rectangular edges of the width direction of bounding rectangles corresponding to it.
Usable satellite SjTwo endpoints in the rectangular edges of bounding rectangles width direction corresponding to it determine the shape based moment The length of shape.If usable satellite SjThe time of observed object region T is earliest and the latestWithUsable satellite SjIt can observe Minimum and maximum lateral swinging angle to target area T is rj -And rj +.With usable satellite SjThe width of bounding rectangles corresponding to it can To be indicated by lateral swinging angle, Wj=(rj --rj +).So with usable satellite SjCorresponding bounding rectangles pot life-posture arrow AmountIt indicates, i.e.,
Offset parameter Δ λ is arranged in S3.3, divides band.
According to set offset parameter Δ λ by each usable satellite SjBounding rectangles corresponding to it are decomposed into band.
In view of the requirement and preference of client, the degree being overlapped between the adjustable adjacent ribbons of offset parameter Δ λ, interband Vacancy can be imaged to avoid caused by the position deviation during satellite actual imaging in overlapping with certain length.Just match For having the satellite of optical camera, it can be indicated by the specific lateral swinging angle that can change.
BandIt can be indicated with used time m- attitude vectors, i.e.,Then each With corresponding usable satellite SjSide-sway angular region be (rj +-rj -)strip, as Δ λ.
Regional aim decomposable process occurs in bounding rectangles, rather than in the whole region of regional aim.Along satellite The vertical direction of track, band is with being alternatively arranged from west to east, until band set is full of entire usable satellite SjIts institute is right The bounding rectangles answered.
S3.4 cutting rod band
After S3.3, it will obtain by usable satellite SjThe band set that bounding rectangles corresponding to it decompose, item It is wide and isometric band with each band in set.It next need to be according to usable satellite SjBounding rectangles corresponding to it are cut Each band is cut to improve Efficient Coverage Rate.
Whether for any one band in band set, first determining has the vertex of regional aim in the band；Then sharp The intersection point for determining target area T and the band is calculated with spherical surface analytic geometry, then by comparing target area T and the band Usable satellite S is found out on the vertex of regional aim present in intersection point and the bandjThe earliest time and latest time passed by To determine band cut-boundary, the cutting to the band is completed.
S3.5 calculates each corresponding SEE time window VTW of band.
VTW can be by determining in given time period [Tbegin,Tend], usable satellite SjCome with target area T visibility To SEE time window [TWbegin,TWend]。
By five steps of above-mentioned S3.1 to S3.5, the decomposition to target area T may be implemented.
More star scheduling problems towards big polygon target observing are converted the set covering problem with multiple constraints by S4, And founding mathematical models.
S4.1 symbol definition
T={ t1,...,t|T|, refer to the observation mission set from different user.For each task tq, define as It is properties:
- q refers to tqCorresponding area observation target designation.
-HqRefer to the resolution requirement that need to obtain image.
-BqRefer to the planning time started.
-EqRefer to the planning end time.
S={ S1,...,Sm, refer to usable satellite set.For every usable satellite Sj, it defines as properties:
-OjRefer to usable satellite SjCircle time set, | Oj| i.e. usable satellite SjCircle time set in circle time number.ojaRefer to Usable satellite SjA-th of circle time.
-MjRefer to usable satellite SjMaximum storage capacity.
-VjRefer to usable satellite SjMaximum electricity.
-GjRefer to usable satellite SjMaximum maneuverability.
·Wj={ wj1,...,wj|Wj|Refer to usable satellite SjCorresponding time window set, | Wj| i.e. usable satellite SjIt is corresponding The number of time window in time window set；wjakRepresent usable satellite SjA circle time in k-th of time window.To each time Window wjak, it defines as properties:
-btjak,etjakIt is time window w respectivelyjakStart and end time.
-mjakRefer in time window wjakMemory capacity shared by corresponding satellite image.
-vjakRefer in time window wjakSatellite electricity spent by the corresponding satellite image of interior shooting.
-gjakRefer to usable satellite SjIn time window wjakInterior posture.
-GSDjakRefer to usable satellite SjIn time window wjakThe resolution ratio of interior corresponding satellite image.
-qjakRefer to time window wjakCorresponding regional aim.
The function applied in modeling process is defined as follows:
- f is the piecewise linear function for calculating observation planned income, and four inflection points are respectively (0,0), (0.4,0.1), (0.7,0.4),(1,1).Function and the target area area of surveillance program covering are positively correlated, and area coverage is bigger, observation program Income is bigger.
- P is the function for calculating band area coverage.
Decision variable is defined as follows:
S4.2 mathematical model
Wherein: objective function (2) is the total revenue for maximizing surveillance program, i.e. the maximization moonscope gross area, wherein Δ P is selector bar band xjakAnd bring observes area amplification.
Constraint (3) each satellite mistake caused by defining due to the satellite long strip observation mode towards big area observation Border at most selects the characteristic of a time window.
Constraint (4) illustrates that satellite memory capacity limits, i.e. storage of the memory shared by satellite storage picture no more than satellite The limit.
Constraint (5) describes the electricity limitation of satellite, i.e. satellite imagery process institute's power consumption is no more than satellite and stores most Big electricity.
Constraint (6) shows time window length no more than the satellite single longest working time.
Constraint (7) and (8) shows that the time window selected in surveillance program will be in planning time section.Constraint (9) shows to defend Star is motor-driven to carry out under itself maneuverability.
Constraint (10) elaborates the resolution requirement to satellite image, that is, passes through current band xijkObtained satellite photo Resolution ratio will meet the image resolution requirement of user's proposition.
S5 solves the mathematical model established in S4 using genetic algorithm, exports the surveillance program being to solve for.
S5.1 coding
Individual coding utilizes certain planning time [B in genetic algorithmq, Eq] all in interior usable satellite set S available defend Star SjThe sum of rail ring number QnumTo construct.Each chromosome length is Qnum, it is available that each gene on chromosome represents one Satellite SjTrack circle time on SEE time window, the sequence of all genes is band selecting sequence, i.e. satellite on chromosome Observation sequence.
S5.2 fitness function
It is realized using entire observation program and standard is calculated as fitness to the coverage rate of target area T, measure Current observation side The situation of Profit of case.The coverage rate of observation program can account for target by the quantity of the point target in observation program in band combination The proportion measurement of the point set of all point target compositions in the T of region.Fitness function is as follows.
Wherein, SchedulekThe feasible observation program that is represented as responding certain observation requirements and generate, the i.e. satellite of selection Band combination.G is the fitness function of algorithm, f be by (0,0), (0.4,0.1), (0.7,0.4), the segmented line that (1,1) determines Property function, promote optimization process to improve coverage rate direction carry out.IqRepresent all point target compositions in the T of target area Point set, and IqlIt is the covered point set of observation program institute, N is the function for calculating the quantity of point target.
The design of S5.3 operator
Single point crossing operator, single-point mutation operator and feasibility operator are applied in problem, can be defined as follows
(1) single point crossing operator: P1 and P2 is parental generation individual, the P1 [N] and P2 [1] ... P2 that is represented by P1 [1] ... [N], wherein selection operator utilizes the form of roulette.Crosspoint K is randomly generated and carries out individual intersection, obtains offspring individual C1 And C2:
C1:P1[1]...P1[K]...P2[K+1]...P2[N]
C2:P2[1]...P2[K]...P1[K+1]...P1[N]
(2) single-point mutation operator: P is parental generation individual, the P [N] that is expressed as P [1] ....Change point K is randomly generated and is become It is different, generate offspring individual C:
C:P[1]...P[K]0...P[K+1]...P[N]
(3) feasibility operator: whether verification scheme is met the constraint such as storage and electricity of satellite by feasibility operator, if It is unsatisfactory for, randomly chooses a parental generation individual and enter in filial generation.
S5.4 determines population quantity
Population quantity parameter and genetic algorithm the number of iterations are set.
Compared with prior art, the present invention can generate following technical effect:
(1) modeling method is provided to more star scheduling problems towards big polygon target observing, mathematics can be carried out to it and is built Mould.
(2) it devises and solves the problems, such as that this three stages solve frame, asked for more stars scheduling towards big polygon target observing The solution of topic provide new resolving ideas, it can be achieved that the generation of more star observation programs and preferably.
(3) present invention solves the satellite image acquisition method that is currently related in environmental protection activity time-consuming, exists and covers The phenomenon that lid is omitted is compared to solve the problem master heuritic approach to be applied based on greedy rule in actual practice, can It obtains for the higher feasible observation program of specific observation area target coverage rate.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is the three regional aim schematic diagrames chosen in an embodiment provided by the invention.
Fig. 3 is to use method proposed by the present invention under three kinds of different application scenes compared with the heuristic calculation based on greedy rule The performance amplification schematic diagram of method.Wherein Fig. 3 (a) is under African prairie application scenarios compared with the heuristic calculation based on greedy rule The performance amplification schematic diagram of method；Fig. 3 (b) is under Amazon forest application scenarios compared with the heuritic approach based on greedy rule Performance amplification schematic diagram；Fig. 3 (c) is under the application scenarios of South China compared with the property of the heuritic approach based on greedy rule It can amplification schematic diagram.
Fig. 4 is shadow of the different parameter settings to algorithm effect when being solved with genetic algorithm provided by the invention Ring effect picture.Fig. 4 (a) is the impact effect figure of crossover probability parameter under African prairie application scenarios；Fig. 4 (b) is African big The impact effect figure of grassland application scenarios lower variation probability parameter；Fig. 4 (c) is that crossover probability is joined under Amazon forest application scenarios Several impact effect figures；Fig. 4 (d) is the impact effect figure of Amazon forest application scenarios lower variation probability parameter；Fig. 4 (e) is The impact effect figure of crossover probability parameter under state's South China's application scenarios；Fig. 4 (f) is South China of state application scenarios lower variation The impact effect figure of probability parameter.
Specific embodiment
With reference to the accompanying drawings of the specification, technical solution of the present invention is further shown and is illustrated.
It referring to Fig.1, is flow chart of the invention.Wherein, problem input includes the observation requirements from client, such as region mesh Information, related satellite information, image request information etc. are marked, the output of problem is the optimal surveillance program met customer need.It should The solution framework of problem is problem pretreatment, problem modeling and three stage of problem solving.Problem pretreatment stage include region from Dispersion and region band decompose, and the problem modelling phase includes problem conversion, building performance evaluation system and establishes problem mathematics Model, problem solving stage include the selection and design of algorithm.
Specifically, a kind of more star dispatching methods towards big polygon target observing, include the following steps；
S1 determines that observation mission determines (the i.e. position in known target region target area T to be observed according to user demand It sets, area)；Determine the satellite parametric reduction information of all usable satellites in usable satellite set S and usable satellite set S.
Wherein: satellite parametric reduction information includes that six orbit parameters are respectively semi-major axis of orbit, orbit inclination angle, track bias Rate, right ascension of ascending node, argument of perigee and true anomaly.
The target area S2 discretization
Target area T to be observed is passed through into the equidistant discretization of dot matrix, all discrete points in target area T to be observed Form point set A.
Each point in point set A is a point target, point target piPosition coordinates useIt indicates, table Show the latitude of its point target in 84 coordinate system of WGS, longitude and altitude.Wherein i=1,2,3 ... n, n are the midpoint point set A The number of target.In this way, target area T to be observed can be indicated with a series of point target.
S3 carries out band decomposition to target area
S3.1 constructor F describes the positional relationship between satellite and point target
When moment t, for the usable satellite S in usable satellite set Sj(wherein j=1,2,3 ... m, m are usable satellite The number of usable satellite in set S.), the spatial position in J2000 geocentric inertial coordinate system isPoint target piIt is in the spatial position of WGS84 coordinate systemIt can be with defending Star SjIt can observe point target piCurrent pose beThen have
Constructor F describes the positional relationship between satellite and point target
Independent variable in function F can be abbreviated as (t, r)T, referred to as when m- orientation vectorThrough target area discretization in S2 Method, target area T to be observed can be indicated with a series of point target.Therefore, target area T to be observed can be indicated Are as follows: { (t, r)1,(t,r)2,...,(t,r)n}。
S3.2 is for each usable satellite S in usable satellite set Sj, calculate SjVisible range to target area T is Visual field (FOV), and calculate the bounding rectangles of FOV Yu the overlapping region target area T.
For each usable satellite S in usable satellite set SjBounding rectangles corresponding to it, the determination of bounding rectangles Intersection point e between FOV and target area T is associated, which can be calculated with spherical surface analytic geometry.
With usable satellite SjSatellite ground tracks (i.e. usable satellite SjStar it is offline) parallel rectangular edges are usable satellites SjThe rectangular edges of the length direction of bounding rectangles corresponding to it, perpendicular to usable satellite SjSatellite ground tracks it is (i.e. available to defend Star SjStar it is offline) side be usable satellite SjThe rectangular edges of the width direction of bounding rectangles corresponding to it.
Usable satellite SjTwo endpoints in the rectangular edges of bounding rectangles width direction corresponding to it determine the shape based moment The length of shape.If usable satellite SjThe time of observed object region T is earliest and the latestWithUsable satellite SjIt can observe Minimum and maximum lateral swinging angle to target area T is rj -And rj +.With satellite SjThe width of bounding rectangles corresponding to it can be by Lateral swinging angle indicates, Wj=(rj --rj +).So with usable satellite SjCorresponding bounding rectangles pot life-orientation vector It indicates, i.e.,
Offset parameter Δ λ is arranged in S3.3, divides band.
According to set offset parameter Δ λ by each usable satellite SjBounding rectangles corresponding to it are decomposed into band.
In view of the requirement and preference of client, the degree being overlapped between the adjustable adjacent ribbons of offset parameter Δ λ, interband Vacancy can be imaged to avoid caused by the position deviation during satellite actual imaging in overlapping with certain length.Just match For having the satellite of optical camera, it can be indicated by the specific lateral swinging angle that can change.
BandIt can be indicated with used time m- attitude vectors, i.e.,Then each With corresponding usable satellite SjSide-sway angular region be (rj +-rj -)strip, as Δ λ.
Regional aim decomposable process occurs in bounding rectangles, rather than in the whole region of regional aim.Along satellite The vertical direction of track, band is with being alternatively arranged from west to east, until band set is full of entire bounding rectangles.
S3.4 cutting rod band
After S3.3, it will obtain by usable satellite SjThe band set that bounding rectangles corresponding to it decompose, item It is wide and isometric band with each band in set.It next need to be according to usable satellite SjBounding rectangles corresponding to it are cut Each band is cut to improve Efficient Coverage Rate.
Whether for any one band in band set, first determining has the vertex of regional aim in the band；Then sharp The intersection point for determining target area T and the band is calculated with spherical surface analytic geometry, then by comparing target area T and the band Usable satellite S is found out on the vertex of regional aim present in intersection point and the bandjThe earliest time and latest time passed by To determine band cut-boundary, the cutting to the band is completed.
S3.5 calculates each corresponding SEE time window VTW of band.
VTW can be by determining in given time period [Tbegin,Tend], usable satellite SjCome with target area T visibility To SEE time window [TWbegin,TWend]。
By five steps of above-mentioned S3.1 to S3.5, the decomposition to target area T may be implemented.
More star scheduling problems towards big polygon target observing are converted the set covering problem with multiple constraints by S4, And founding mathematical models.
S4.1 symbol definition
T={ t1,...,t|T|, refer to the observation mission set from different user.For each task tq, define as It is properties:
- q refers to tqCorresponding area observation target designation.
-HqRefer to the resolution requirement that need to obtain image.
-BqRefer to the planning time started.
-EqRefer to the planning end time.
S={ S1,...,Sm, refer to usable satellite set.For every usable satellite Sj, it defines as properties:
-OjRefer to usable satellite SjCircle time set, | Oj| i.e. usable satellite SjCircle time set in circle time number.ojaRefer to Usable satellite SjA-th of circle time.
-MjRefer to usable satellite SjMaximum storage capacity.
-VjRefer to usable satellite SjMaximum electricity.
-GjRefer to usable satellite SjMaximum maneuverability.
·Wj={ wj1,...,wj|Wj|Refer to usable satellite SjCorresponding time window set, | Wj| i.e. usable satellite SjIt is corresponding The number of time window in time window set；wjakRepresent usable satellite SjA circle time in k-th of time window.To each time Window wjak, it defines as properties:
-btjak,etjakIt is time window w respectivelyjakStart and end time.
-mjakRefer in time window wjakMemory capacity shared by corresponding satellite image.
-vjakRefer in time window wjakSatellite electricity spent by the corresponding satellite image of interior shooting.
-gjakRefer to usable satellite SjIn time window wjakInterior posture.
-GSDjakRefer to usable satellite SjIn time window wjakThe resolution ratio of interior corresponding satellite image.
-qjakRefer to time window wjakCorresponding regional aim.
The function applied in modeling process is defined as follows:
- f is the piecewise linear function for calculating observation planned income, and four inflection points are respectively (0,0), (0.4,0.1), (0.7,0.4),(1,1).Function and the target area area of surveillance program covering are positively correlated, and area coverage is bigger, observation program Income is bigger.
- P is the function for calculating band area coverage.
Decision variable is defined as follows:
S4.2 mathematical model
Wherein: objective function (2) is the total revenue for maximizing surveillance program, i.e. the maximization moonscope gross area, wherein Δ P is selector bar band xjakAnd bring observes area amplification.
Constraint (3) each satellite mistake caused by defining due to the satellite long strip observation mode towards big area observation Border at most selects the characteristic of a time window.
Constraint (4) illustrates that satellite memory capacity limits, i.e. storage of the memory shared by satellite storage picture no more than satellite The limit.
Constraint (5) describes the electricity limitation of satellite, i.e. satellite imagery process institute's power consumption is no more than satellite and stores most Big electricity.
Constraint (6) shows time window length no more than the satellite single longest working time.
Constraint (7) and (8) shows that the time window selected in surveillance program will be in planning time section.Constraint (9) shows to defend Star is motor-driven to carry out under itself maneuverability.
Constraint (10) elaborates the resolution requirement to satellite image, that is, passes through current band xijkObtained satellite photo Resolution ratio will meet the image resolution requirement of user's proposition.
S5 solves the mathematical model established in S4 using genetic algorithm, exports the surveillance program being to solve for.
S5.1 coding
Individual coding utilizes certain planning time [B in genetic algorithmq, Eq] all in interior usable satellite set S available defend Star SjThe sum of rail ring number QnumTo construct.Each chromosome length is Qnum, it is available that each gene on chromosome represents one Satellite SjTrack circle time on SEE time window, the sequence of all genes is band selecting sequence, i.e. satellite on chromosome Observation sequence.
S5.2 fitness function
It is realized using entire observation program and standard is calculated as fitness to the coverage rate of target area T, measure Current observation side The situation of Profit of case.The coverage rate of observation program can account for target by the quantity of the point target in observation program in band combination The proportion measurement of the point set of all point target compositions in the T of region.Fitness function is as follows.
Wherein, SchedulekThe feasible observation program that is represented as responding certain observation requirements and generate, the i.e. satellite of selection Band combination.G is the fitness function of algorithm, f be by (0,0), (0.4,0.1), (0.7,0.4), the segmented line that (1,1) determines Property function, promote optimization process to improve coverage rate direction carry out.IqRepresent all point target compositions in the T of target area Point set, and IqlIt is the covered point set of observation program institute, N is the function for calculating the quantity of point target.
The design of S5.3 operator
Single point crossing operator, single-point mutation operator and feasibility operator are applied in problem, can be defined as follows
(1) single point crossing operator: P1 and P2 is parental generation individual, the P1 [N] and P2 [1] ... P2 that is represented by P1 [1] ... [N], wherein selection operator utilizes the form of roulette.Crosspoint K is randomly generated and carries out individual intersection, obtains offspring individual C1 And C2:
C1:P1[1]...P1[K]...P2[K+1]...P2[N]
C2:P2[1]...P2[K]...P1[K+1]...P1[N]
(2) single-point mutation operator: P is parental generation individual, the P [N] that is expressed as P [1] ....Change point K is randomly generated and is become It is different, generate offspring individual C:
C:P[1]...P[K]0...P[K+1]...P[N]
(3) feasibility operator: whether verification scheme is met the constraint such as storage and electricity of satellite by feasibility operator, if It is unsatisfactory for, randomly chooses a parental generation individual and enter in filial generation.
Wherein, the crossover probability p of crossover operatorcWith the mutation probability p of mutation operatormBy by way of test experiments into Row is chosen, and the value that selection utmostly improves this paper algorithm search effect in its (0,1) range is general as the intersection of algorithm Rate pcWith mutation probability pm。
S5.4 determines population quantity
Population quantity parameter and genetic algorithm the number of iterations are set.
A specific application example is provided below:
Increasingly important role is played in environmental protection in view of present EOS, the present embodiment selects high vegetation to cover Cover region selects three high vegetative coverages from African prairie, Amazon forest and South China respectively as observed object Region, as shown in Figure 2.The geographical location of three targets and essential information are as shown in table 1.
The position of 1 target area of table and size
For each target area, scheduling time is one day, is carried out continuously 5 scheduling, from 0:00 on July 22nd, 2017: On July 27th, 00 to 2017,0:00:00, was during which related to 14 optical satellites from China Satecom's platform.Satellite is in space Position can there are six orbit parameter determine, respectively semi-major axis of orbit, orbit inclination angle, orbital eccentricity, right ascension of ascending node, Argument of perigee and true anomaly.14 satellite orbit parameters involved in the present embodiment are as shown in table 2.
2 satellite orbit parameter of table
The parameter setting of genetic algorithm is as follows in the present embodiment: population quantity 100, the number of iterations 500, satellite single Longest imaging time is 10 minutes, offset parameter 1.8.
Table 3 gives genetic algorithm proposed by the present invention and the heuritic approach based on greedy rule in 3 kinds of different scenes Under as a result, the result of each scene be 20 times operation average values.One column of performance indicates that both algorithms will be at three Main aspect is assessed, i.e. DT, CR and Sec.DT refers to the observation time of the optimal scheduling of generation, indicates to towards big region The efficiency of target observation.CR indicate the observation program that two kinds of algorithms obtain within the scope of specific planning time to target area Maximal cover rate.Here, use 0 as each observing time table minimum yield, 100% be used as maximum return.Sec is single with the second Position computational algorithm runing time.DT1, CR1, Sec1 assess the performance of proposed genetic algorithm, and DT2, CR2, Sec2 are assessed The performance of heuritic approach based on greedy rule.According to three regional positions, i.e., African, Amazon and China devise Different application scenarios.D1, D2, D3, D4 and D5 are arranged from 0:00:00 on July 27,0:00:00 to 2017 years on the 22nd July in 2017 Between describe 5 independent planning sections.Each scene is assessed in this 5 time ranges.
The operation result of 3 two kinds of algorithms of table compares
As shown in table 3, it is found that genetic algorithm proposed by the present invention can produce than the heuritic approach based on greedy rule Better solution.In view of scheduling process is using the coverage rate for maximizing regional aim as target, the genetic algorithm that is proposed Compared with the heuritic approach in a practical situation commonly based on greedy rule, Image Acquisition is largely improved Performance.In order to accurately disclose method proposed by the invention in the advantage of different scenes, indicated using three histograms, such as Shown in Fig. 3.For each example, gross profit (i.e. coverage rate) is presented on the y axis, and planning time is presented in x-axis.As a result The increase of display algorithm performance will not be largely to calculate the time as cost, and the longest calculating time has reached 223.2 seconds, this is in energy In the range of enough receiving.
It is the scene of observed object in the same time using South China from the point of view of the optimal coverage effect of three scenes There is apparent advantage compared to other two scenes in section.This may be because the satellite for having 14 satellites to be all from China is flat Platform, it is intended to the whole distract on observation China and periphery.On the other hand, this observation performance for showing multi-satellite to be improved, with it His coordination and cooperation of national satellite platform satellite is a feasible approach.
It is also tested for influence of the different parameters setting to algorithm effect is proposed simultaneously, to find out most suitable algorithm parameter, Intersect and mutation probability, design implement two test experiments.Firstly, having carried out a test experiments to assess mutation probability Influence to algorithm performance, crossover probability is fixed as 0.9 at this time.Setting gradually mutation probability is 0.001,0.005,0.01, 0.03,0.05,0.08,0.1, to assess the algorithm efficiency under every kind of scene, as a result as shown in Fig. 4 (b) (d) (f).In addition, into Go that another tests influence to assess crossover probability to algorithm performance, mutation probability is fixed as 0.1 at this time.Set gradually friendship Pitching probability is 0.7,0.75,0.8,0.85,0.9,0.95, as a result as shown in Fig. 4 (a) (c) (e), it is known that the present invention is mentioned The genetic algorithm of confession, optimal mutation probability section and crossover probability are respectively [0.08,0.1] and [0.85,0.9].This is just It is the reason of mutation probability and crossover probability are initially fixed as 0.1 and 0.9.
For three test scenes, the calculating time of the method for the present invention consumption, this showed in practice never more than 5 minutes The present invention does well in terms of efficiency and reliability.This obtains useful information and image for ground maneuvers personnel, to support The monitoring of the large area such as Global Forests provides possibility.
In conclusion the present invention designed for more star scheduling problems towards big polygon target observing is solving the problems, such as Aspect of performance better than the heuritic approach based on greedy rule in currently practical application, the present invention can produce within a short period of time The satellite imagery scheme of raw high coverage rate, provides preferably solution party for the satellite imagery difficult problem in environmental protection activity Case.
The foregoing is merely a preferred embodiment of the present invention, are not intended to restrict the invention, for this field For technical staff, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any Modification, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.

Claims (6)

1. a kind of more star dispatching methods towards big polygon target observing, which comprises the following steps:
S1 determines that observation mission determines target area T to be observed according to user demand, determine usable satellite set S and The satellite parametric reduction information of all usable satellites in usable satellite set S；
The target area S2 discretization；
Target area T to be observed is passed through into the equidistant discretization of dot matrix, all discrete points composition in target area T to be observed Point set A；
S3 carries out band decomposition to target area；
More star scheduling problems towards big polygon target observing are converted the set covering problem with multiple constraints by S4, and builds Vertical mathematical model；
S5 solves the mathematical model established in S4 using genetic algorithm, exports the surveillance program being to solve for.
2. more star dispatching methods according to claim 1 towards big polygon target observing, which is characterized in that in S1, defend Star parameter information includes six orbit parameters, respectively semi-major axis of orbit, orbit inclination angle, orbital eccentricity, right ascension of ascending node, close Place argument and true anomaly.
3. more star dispatching methods according to claim 1 towards big polygon target observing, which is characterized in that in S2, point The each point collected in A is a point target, point target piPosition coordinates useIt indicates, Respectively indicate the latitude of its point target in 84 coordinate system of WGS, longitude and altitude；Wherein i=1,2,3 ... n, n are point set The number of point target in A；In this way, target area T to be observed is indicated with a series of point target.
4. more star dispatching methods according to claim 3 towards big polygon target observing, which is characterized in that the realization of S3 Method is as follows:
S3.1 constructor F describes the positional relationship between satellite and point target
When moment t, for the usable satellite S in usable satellite set Sj, wherein j=1,2,3 ... m, m are usable satellite set S The number of middle usable satellite, usable satellite SjIts spatial position in J2000 geocentric inertial coordinate system isPoint target piIt is in the spatial position of WGS84 coordinate systemIt can be with defending Star SjIt can observe point target piCurrent pose beThen have
Constructor F describes the positional relationship between satellite and point target
Independent variable in function F can be abbreviated as (t, r)T, referred to as when m- orientation vectorThrough target area discretization side in S2 Method, target area T to be observed can be indicated with a series of point target；Therefore, target area T to be observed can be indicated Are as follows: { (t, r)1,(t,r)2,...,(t,r)n}；
S3.2 is for each usable satellite S in usable satellite set Sj, calculate SjTo visible range, that is, visual field of target area T (FOV), and the bounding rectangles of FOV Yu the overlapping region target area T are calculated；
For each usable satellite S in usable satellite set SjBounding rectangles corresponding to it, the determination of bounding rectangles and FOV Intersection point e between the T of target area is associated, which can be calculated with spherical surface analytic geometry；
With usable satellite SjThe parallel rectangular edges of satellite ground tracks be usable satellite SjThe length of bounding rectangles corresponding to it The rectangular edges in direction, perpendicular to usable satellite SjThe sides of satellite ground tracks be usable satellite SjBounding rectangles corresponding to it Width direction rectangular edges；
Usable satellite SjTwo endpoints in the rectangular edges of bounding rectangles width direction corresponding to it determine the length of the bounding rectangles Degree；If usable satellite SjThe time of observed object region T is earliest and the latestWithUsable satellite SjIt can observe target The minimum and maximum lateral swinging angle of region T is rj -And rj +；With usable satellite SjThe width of bounding rectangles corresponding to it can be by side Pivot angle indicates, Wj=(rj --rj +)；So with usable satellite SjCorresponding bounding rectangles pot life-orientation vectorTable Show, as
Offset parameter Δ λ is arranged in S3.3, divides band；
According to set offset parameter Δ λ by each usable satellite SjBounding rectangles corresponding to it are decomposed into band；
BandIt can be indicated with used time m- attitude vectors, i.e.,Then each band is corresponding Usable satellite SjSide-sway angular region be (rj +-rj -)strip, as Δ λ；
Along the vertical direction of satellite orbit, band is with being alternatively arranged from west to east, until band set is full of entire available Satellite SjBounding rectangles corresponding to it；
S3.4 cutting rod band
After S3.3, it will obtain by usable satellite SjThe band set that bounding rectangles corresponding to it decompose, band set In each band be wide and isometric band；It next need to be according to usable satellite SjBounding rectangles corresponding to it cut each item Band is to improve Efficient Coverage Rate；
Whether for any one band in band set, first determining has the vertex of regional aim in the band；Then ball is utilized Face analytic geometry calculates the intersection point for determining target area T and the band, then by comparing the intersection point of target area T and the band And usable satellite S is found out on the vertex of regional aim present in the bandjThe earliest time and latest time passed by are with true Determine band cut-boundary, completes the cutting to the band；
S3.5 calculates each corresponding SEE time window VTW of band；
VTW can be by determining in given time period [Tbegin,Tend], usable satellite SjObtaining with target area T visibility can See time window [TWbegin,TWend]；
By five steps of above-mentioned S3.1 to S3.5, the decomposition to target area T is realized.
5. more star dispatching methods according to claim 4 towards big polygon target observing, which is characterized in that the realization of S4 Method is as follows:
S4.1 symbol definition
T={ t1,...,t|T|, refer to the observation mission set from different user；For each task tq, define such as subordinate Property:
Q refers to tqCorresponding area observation target designation；
HqRefer to the resolution requirement that need to obtain image；
BqRefer to the planning time started；
EqRefer to the planning end time；
S={ S1,...,Sm, refer to usable satellite set；For every usable satellite Sj, it defines as properties:
OjRefer to usable satellite SjCircle time set, | Oj| i.e. usable satellite SjCircle time set in circle time number；ojaRefer to available defend Star SjA-th of circle time；
MjRefer to usable satellite SjMaximum storage capacity；
VjRefer to usable satellite SjMaximum electricity；
GjRefer to usable satellite SjMaximum maneuverability；
Wj={ wj1,...,wj|Wj|Refer to usable satellite SjCorresponding time window set, | Wj| i.e. usable satellite SjCorresponding time window The number of time window in set；wjakRepresent usable satellite SjA circle time in k-th of time window；
To each time window wjak, it defines as properties:
btjak,etjakIt is time window w respectivelyjakStart and end time；
mjakRefer in time window wjakMemory capacity shared by corresponding satellite image；
vjakRefer in time window wjakSatellite electricity spent by the corresponding satellite image of interior shooting；
gjakRefer to usable satellite SjIn time window wjakInterior posture；
GSDjakRefer to usable satellite SjIn time window wjakThe resolution ratio of interior corresponding satellite image；
qjakRefer to time window wjakCorresponding regional aim；
The function applied in modeling process is defined as follows:
F is the piecewise linear function for calculating observation planned income, and four inflection points are respectively (0,0), (0.4,0.1), (0.7, 0.4),(1,1)；Function and the target area area of surveillance program covering are positively correlated, and area coverage is bigger, and observation program income is got over Greatly；
P is the function for calculating band area coverage.
Decision variable is defined as follows:
S4.2 mathematical model
Wherein: objective function (2) is the total revenue for maximizing surveillance program, i.e. the maximization moonscope gross area, and wherein Δ P is Selector bar band xjakAnd bring observes area amplification；
Each satellite caused by (3) are defined due to the satellite long strip observation mode towards big area observation is constrained to pass by most The characteristic of one time window of more options；
Constraint (4) illustrates that satellite memory capacity limits, i.e. storage limit of the memory shared by satellite storage picture no more than satellite；
Constraint (5) describes the electricity limitation of satellite, i.e. satellite imagery process institute's power consumption is no more than the maximum electricity that satellite stores Amount；
Constraint (6) shows time window length no more than the satellite single longest working time；
Constraint (7) and (8) shows that the time window selected in surveillance program will be in planning time section；
Constraint (9), which shows that satellite is motor-driven, to be carried out under itself maneuverability；
Constraint (10) elaborates the resolution requirement to satellite image, that is, passes through current band xijkObtained satellite photo is differentiated Rate will meet the image resolution requirement of user's proposition.
6. more star dispatching methods according to claim 5 towards big polygon target observing, which is characterized in that the realization of S5 Method is as follows:
S5.1 coding
The coding of individual utilizes certain planning time [Bq, Eq] all usable satellite S in interior usable satellite set SjRail ring number The sum of QnumTo construct；Each chromosome length is Qnum, each gene on chromosome represents a usable satellite SjRail ring SEE time window on secondary, the sequence of all genes is band selecting sequence on chromosome, i.e. moonscope sequence；
S5.2 fitness function
Fitness function is as follows:
Wherein, SchedulekThe feasible observation program that is represented as responding certain observation requirements and generate, i.e. the satellite band of selection Combination；G is fitness function, and f is by (0,0), and (0.4,0.1), (0.7,0.4), the piecewise linear function that (1,1) determines promotees It is carried out into optimization process to the direction for improving coverage rate；IqIt is to represent the point set of all point targets composition in the T of target area, and Iql It is the covered point set of observation program institute, N is the function for calculating the quantity of point target；
The design of S5.3 operator
It is as follows to define single point crossing operator, single-point mutation operator and feasibility operator:
(1) single point crossing operator: P1 and P2 is parental generation individual, the P1 [N] and P2 [1] ... P2 [N] that is represented by P1 [1] ..., Middle selection operator utilizes the form of roulette；Crosspoint K is randomly generated and carries out individual intersection, obtains offspring individual C1 and C2:
C1:P1[1]...P1[K]...P2[K+1]...P2[N]
C2:P2[1]...P2[K]...P1[K+1]...P1[N]
(2) single-point mutation operator: P is parental generation individual, the P [N] that is expressed as P [1] ....Change point K is randomly generated to go forward side by side row variation, Generate offspring individual C:
C:P[1]...P[K]0...P[K+1]...P[N]
(3) feasibility operator: whether verification scheme is met the constraint such as storage and electricity of satellite by feasibility operator, if discontented Sufficient then one parental generation individual of random selection enters in filial generation；
S5.4 determines population quantity
Population quantity parameter and genetic algorithm the number of iterations are set.
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